For probe-based spatial transcriptomics, the number and identity of genes that have probesets is limited. This script queries how this restricted gene set might perform in cell identification by routine clustering.

Environment

library(future)
plan("multiprocess", workers = 4)
Error: No such strategy for futures: ‘multiprocess’

Read in IPF Cell Atlas (GSE136831)

This notebook depends on the output of the read-in script. for convenience, can bypass script by loading stored Seurat object directly.

# This is more reliable
#source(knitr::purl("IPF-Cell-Atlas__read-in.Rmd", quiet=TRUE))
knitr::knit("IPF-Cell-Atlas__read-in.Rmd", output = tempfile())
# faster
load('ipfatlas.cells_fromd8d92b9.Robj')

Simulate Xenium lung panel 1

This is the panel used in the initial trial runs with the genomics CoLabs.

xenium.panel1.genes <- read.csv('Xenium_panel_order_08_10_23.csv', header = TRUE)$Gene
ipfatlas.xen1panel.cells <- subset(ipfatlas.cells, features=xenium.panel1.genes)

Some genes in the xenium panel are not in the IPF dataset. Not sure why the dataset in GSE136831 is missing these genes. so actual list is a bit shorter. These are the missing genes:

setdiff(xenium.panel1.genes, rownames(ipfatlas.xen1panel.cells))

Standard clustering

print('hello')
[1] "hello"
ipfatlas.xen1panel.cells <- NormalizeData(ipfatlas.xen1panel.cells)
Normalizing layer: counts
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
ipfatlas.xen1panel.cells <- FindVariableFeatures(ipfatlas.xen1panel.cells)
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
ipfatlas.xen1panel.cells <- ScaleData(ipfatlas.xen1panel.cells, features=rownames(ipfatlas.xen1panel.cells))
Centering and scaling data matrix

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  |======================================================================================================================| 100%
ipfatlas.xen1panel.cells <- RunPCA(ipfatlas.xen1panel.cells)
PC_ 1 
Positive:  ELF3, AGR3, ENAH, PDE4D, TACSTD2, CLDN4, CDH1, AGR2, TMC5, EPCAM 
       RORA, EGFR, EHF, KRT8, CD24, TC2N, CTTN, CD247, KLF5, NTN4 
       KRT7, MALL, FOXJ1, TMPRSS2, CCDC78, MTUS1, F3, GZMB, SFTA2, TFF3 
Negative:  AIF1, MARCO, VSIG4, S100A9, MS4A4A, CTSL, MCEMP1, GLIPR2, SLC1A3, FCGR3A 
       CSTA, S100A8, LGALS3, RETN, ANPEP, CD86, KLF4, TREM2, CD163, SYK 
       ADAM17, IFI6, NCEH1, CD14, FCGR1A, HAVCR2, AQP9, CLEC12A, LILRB4, MIS18BP1 
PC_ 2 
Positive:  CD247, RUNX3, STAT4, GZMB, NKG7, GZMA, CD3D, CD2, KLRB1, CD3E 
       KLRD1, LCK, CD8A, IQGAP2, GPR171, SAMD3, KLRC1, IFNG, CD8B, PIM2 
       TBX21, CTLA4, GATA3, CD28, FGFBP2, CXCR6, CD40LG, TNFAIP3, LAG3, TNFRSF18 
Negative:  ELF3, AGR3, CDH1, CLDN4, TACSTD2, TMC5, AGR2, KRT8, LGALS3, EPCAM 
       EHF, ENAH, MTUS1, KLF5, KRT7, LGALS3BP, EGFR, CTTN, ATP1B1, CD24 
       MYO6, MALL, NCEH1, SFTA2, TMPRSS2, SEMA3C, F3, EGLN3, CCDC78, NTN4 
PC_ 3 
Positive:  AGR3, ELF3, CDH1, TMC5, AGR2, CLDN4, TACSTD2, EPCAM, EHF, KRT8 
       CCDC78, FOXJ1, CD24, TMPRSS2, SFTA2, KRT7, KLF5, SFTPD, ALOX15, GKN2 
       CEACAM6, MAP7, DUOX1, IQGAP2, MUC16, FAM184A, BCAS1, ITGB6, CP, ATP1B1 
Negative:  SFRP2, COL1A1, COL5A2, THY1, FBN1, GNG11, CRISPLD2, THBS2, MEDAG, CD34 
       POSTN, SLIT3, SCN7A, PLA2G2A, RAMP2, CTHRC1, ADAMTS1, INMT, PDGFRB, NID1 
       PDPN, APOD, CLDN5, IL33, DCLK1, LTBP2, MFAP5, SVEP1, IGF1, TMEM100 
PC_ 4 
Positive:  CD247, CD3D, LGALS3BP, GZMB, GZMA, RBP4, CD2, CD3E, PCOLCE2, KLRD1 
       IL7R, NKG7, IQGAP2, KLRB1, LCK, FCGR3A, CD8A, ATP1B1, RUNX3, NCEH1 
       FGFBP2, SAMD3, MCEMP1, VSIG4, FABP3, STAT4, GPI, CD8B, MARCO, IFI6 
Negative:  FCN1, S100A12, ADAM28, GPR183, CD300E, HIF1A, VEGFA, TNFRSF13C, AREG, MS4A1 
       CD79A, CSF3R, BANK1, CLDN5, CD14, S100A8, SERPINB2, LILRB2, LILRA5, CLEC10A 
       RAMP2, MET, SELL, SMAD3, VWF, CLEC4E, TNFRSF13B, SHANK3, CD1C, FCER1A 
PC_ 5 
Positive:  CLDN5, RAMP2, VWF, SHANK3, GNG11, ADGRL4, ACKR1, MMRN1, FCN3, PLVAP 
       TMEM100, IL33, TM4SF18, KDR, SMAD6, SELP, ITGA6, TFF3, POSTN, LYVE1 
       SELE, ICA1, MTUS1, PROX1, CA4, RND1, APOLD1, MYO6, STC1, CD34 
Negative:  COL1A1, SFRP2, CRISPLD2, COL5A2, THY1, THBS2, SCN7A, PLA2G2A, SLIT3, DCLK1 
       PDGFRB, SVEP1, MEDAG, MFAP5, LTBP2, GPR183, CXCL14, RARRES1, FCN1, AREG 
       VEGFA, INMT, TSPAN8, COL8A1, PI16, APOD, PAMR1, FBN1, S100A12, CD300E 
ipfatlas.xen1panel.cells <- RunUMAP(ipfatlas.xen1panel.cells, dims=1:30)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session22:57:20 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
22:57:20 Read 253424 rows and found 30 numeric columns
22:57:20 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
22:57:20 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
22:58:04 Writing NN index file to temp file /tmp/RtmpvuYL8i/filef3dd462abf0f
22:58:04 Searching Annoy index using 1 thread, search_k = 3000
23:00:24 Annoy recall = 100%
23:00:25 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
23:00:41 Initializing from normalized Laplacian + noise (using RSpectra)
23:01:17 Commencing optimization for 200 epochs, with 12124680 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:03:40 Optimization finished
DimPlot(ipfatlas.xen1panel.cells, group.by='CellType_Category')
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

DimPlot(ipfatlas.xen1panel.cells, group.by='Manuscript_Identity')
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

DimPlot(ipfatlas.xen1panel.cells, group.by='Subclass_Cell_Identity')
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

DimPlot(ipfatlas.xen1panel.cells, group.by='Subject_Identity')
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

DimPlot(ipfatlas.xen1panel.cells, group.by='Disease_Identity')
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Subset epithelium

ipfatlas.xen1panel.epi.cells <- subset(ipfatlas.xen1panel.cells, subset=CellType_Category=='Epithelial')
ipfatlas.xen1panel.epi.cells <- NormalizeData(ipfatlas.xen1panel.epi.cells)
Normalizing layer: counts
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
ipfatlas.xen1panel.epi.cells <- FindVariableFeatures(ipfatlas.xen1panel.epi.cells)
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
ipfatlas.xen1panel.epi.cells <- ScaleData(ipfatlas.xen1panel.epi.cells, features=rownames(ipfatlas.xen1panel.epi.cells))
Centering and scaling data matrix

  |                                                                                                                           
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  |=====================================================================================================================| 100%
ipfatlas.xen1panel.epi.cells <- RunPCA(ipfatlas.xen1panel.epi.cells)
Warning: The following 1 features requested have zero variance; running reduction without them: ICA1PC_ 1 
Positive:  SFTA2, SFTPD, PEBP4, ABCA3, GKN2, CSF3R, MALL, F3, ITGB6, ALOX15B 
       FASN, ETV5, AREG, DAPK2, COL8A1, PLA2G4F, ERN1, HIF1A, RND1, MYC 
       DMBT1, STEAP4, CFTR, AGER, VEGFA, NFKB1, S100A9, TMPRSS2, HP, MET 
Negative:  CCDC78, FOXJ1, CD24, SEMA3C, ENAH, ALOX15, POU2AF1, TFF3, BCAS1, MUC16 
       FGFBP2, TMC5, TP73, FOXN3, CP, IQGAP2, SLC2A1, CCNA1, RARRES1, PAMR1 
       RGS5, CD4, ITGB4, AGR3, IFI6, SVEP1, CYP2F1, COL5A2, APOD, EPCAM 
PC_ 2 
Positive:  PDPN, PLA2G2A, AGER, UPK3B, MEDAG, SCEL, WT1, VCAM1, COL8A1, SEMA3B 
       THBS2, INMT, SLIT3, GNG11, CRISPLD2, DAPK2, RBP4, DES, PTGS1, WFS1 
       CD34, VEGFA, AQP9, KRT5, TP63, MMP1, PCOLCE2, IL1RL1, POSTN, DIRAS3 
Negative:  AGR2, ELF3, CLDN4, AGR3, XBP1, LGALS3, TACSTD2, BAIAP2L1, EPCAM, KRT8 
       TMC5, S100A9, EHF, STEAP4, TSPAN8, CP, CDH1, TFF3, SEC11C, MTUS1 
       PRDX6, MALL, MUC5B, ATP1B1, KLF5, MET, LTF, FOXN3, GDF15, CYP2F1 
PC_ 3 
Positive:  PDE4D, ABCA3, SFTPD, AGR3, CSF3R, PEBP4, GKN2, CCDC78, SFTA2, ALOX15B 
       FAM184A, TMC5, PLA2G4F, FOXJ1, CD38, FASN, ELF3, TMPRSS2, NTN4, DUOX1 
       ETV5, RND1, GLCCI1, TP73, POU2AF1, EGLN3, DMBT1, EPCAM, CDH1, TC2N 
Negative:  PLA2G2A, KRT17, KLK11, MEDAG, CXCL6, KRT5, PDPN, VCAM1, KRT15, WT1 
       TSPAN8, IGFBP3, ADAM28, UPK3B, GNG11, PIM1, COL1A1, RARRES1, CRISPLD2, KLF4 
       SLIT3, RORA, SERPINB2, RBP4, HP, KRT7, THBS2, KDR, LTF, MUC5B 
PC_ 4 
Positive:  KRT17, KRT15, KRT5, KRT7, ADAM28, CEACAM6, TSPAN8, TP63, MMP1, KLK11 
       CDH1, IL33, MUC5B, TACSTD2, DAPK2, SLC15A2, TRPC6, F3, ERN2, CXCL6 
       CXCL14, LTF, ANPEP, DCLK1, EHF, ITGB4, ELF3, CSTA, AGER, SPDEF 
Negative:  PLA2G2A, MEDAG, VCAM1, WT1, HP, GNG11, SLIT3, THBS2, RBP4, INMT 
       CRISPLD2, ALOX15, CD34, SEMA3C, PDPN, COL8A1, SVEP1, ADAMTS1, CCDC78, AQP9 
       RARRES1, MUC16, UPK3B, FOXJ1, DES, PRDX6, EGLN3, TNFAIP3, COL5A2, PCOLCE2 
PC_ 5 
Positive:  KRT5, TP63, KRT17, KRT15, ITGA6, MMP1, PDE4D, MET, CSF3R, MTUS1 
       ETV5, EHF, HIF1A, TRPC6, NFKB1, IL33, ERN1, RORA, PLA2G4F, ADAM17 
       ALOX15B, AREG, MYC, ENAH, POSTN, IL4R, ADAMTS1, TNFAIP3, CXCL14, TGFB1 
Negative:  AGER, CEACAM6, SCEL, WFS1, KRT7, SEMA3B, VEGFA, UPK3B, DAPK2, COL8A1 
       SFTA2, LGALS3BP, IFI6, EPCAM, TACSTD2, KLK11, KRT8, AGR3, SLC15A2, LGALS3 
       PDPN, PARP14, CD24, PRDX6, OTUD7B, DCLK1, TSPAN8, MUC5B, GLCCI1, TFF3 
ipfatlas.xen1panel.epi.cells <- RunUMAP(ipfatlas.xen1panel.epi.cells, dims=1:30)
23:49:18 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
23:49:18 Read 13370 rows and found 30 numeric columns
23:49:18 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
23:49:18 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:49:19 Writing NN index file to temp file /tmp/RtmpvuYL8i/filef3dd2d8fb7a6
23:49:19 Searching Annoy index using 1 thread, search_k = 3000
23:49:23 Annoy recall = 100%
23:49:24 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
23:49:27 Initializing from normalized Laplacian + noise (using RSpectra)
23:49:27 Commencing optimization for 200 epochs, with 614028 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:49:34 Optimization finished
DimPlot(ipfatlas.xen1panel.epi.cells, group.by='Manuscript_Identity')

DimPlot(ipfatlas.xen1panel.epi.cells, group.by='Subclass_Cell_Identity')

DimPlot(ipfatlas.xen1panel.epi.cells, group.by='Subject_Identity')

DimPlot(ipfatlas.xen1panel.epi.cells, group.by='Disease_Identity')

Check fibrotic DATP expression

FeaturePlot(ipfatlas.xen1panel.epi.cells, features=c('CLDN4','KRT7','LGALS3','GDF15'), order=T, max.cutoff = 'q99', min.cutoff='q1')

FeaturePlot(ipfatlas.xen1panel.epi.cells, features=c('KRT17','KRT5','TP63','PDPN'), order=T, max.cutoff = 'q99', min.cutoff='q1')

Subset fibroblasts

ipfatlas.xen1panel.fib.cells <- subset(ipfatlas.xen1panel.cells, subset=CellType_Category=='Stromal')
ipfatlas.xen1panel.fib.cells <- NormalizeData(ipfatlas.xen1panel.fib.cells)
Normalizing layer: counts
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
ipfatlas.xen1panel.fib.cells <- FindVariableFeatures(ipfatlas.xen1panel.fib.cells)
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
ipfatlas.xen1panel.fib.cells <- ScaleData(ipfatlas.xen1panel.fib.cells, features=rownames(ipfatlas.xen1panel.fib.cells))
Centering and scaling data matrix

  |                                                                                                                           
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  |                                                                                                                           
  |=====================================================================================================================| 100%
ipfatlas.xen1panel.fib.cells <- RunPCA(ipfatlas.xen1panel.fib.cells)
Warning: The following 9 features requested have zero variance; running reduction without them: TNF, ATP1B1, ENAH, IRF3, GZMK, IFNA1, IL17A, ARL14, ASCL3PC_ 1 
Positive:  MFAP5, SFRP2, PLA2G2A, MEDAG, FBN1, DCLK1, CD34, RARRES1, SEMA3C, PDPN 
       APOD, PCOLCE2, PI16, THBS2, KLF4, F3, LGALS3, IGF1, CXCL14, CTSL 
       VEGFA, NFKB1, THY1, MYC, PIM1, COL1A1, RAMP2, IL33, LGALS3BP, XBP1 
Negative:  MYH11, DGKG, DES, PLN, HIGD1B, RGS5, KCNK3, LMOD1, ITGA1, CSPG4 
       TRPC6, LAMC3, RERGL, PDGFRB, LYVE1, CNN1, APOLD1, FKBP5, LGR6, NID1 
       FCMR, STAT4, ADAMTS1, SLIT3, INMT, CRISPLD2, SCN7A, CD4, NTRK2, P2RX1 
PC_ 2 
Positive:  POSTN, COL8A1, COL1A1, COL5A2, CTHRC1, PDE4D, SCN7A, LTBP2, RORA, FOXN3 
       FSCN1, THY1, VCAM1, CD4, ITGA1, CSTA, WNT2, SPRY1, GJA5, SAMD3 
       ADAM17, FGFR4, MIS18BP1, IGFBP3, HIF1A, DIRAS3, TGFB1, EPCAM, SLC1A3, DPP6 
Negative:  MFAP5, PI16, PLA2G2A, PCOLCE2, CD34, MYC, RAMP2, NTN4, DGKG, MYH11 
       F3, MEDAG, NID1, DES, CXCL14, GNG11, ADAMTS1, IL6, RERGL, CRISPLD2 
       KLF4, NTRK2, PLN, HIGD1B, STEAP4, APOLD1, APOD, FABP3, RGS5, VEGFA 
PC_ 3 
Positive:  BAIAP2L1, DGKG, CNN1, PDGFRB, MYH11, APOLD1, HIGD1B, ATF4, RGS5, LYVE1 
       PLN, PRDX6, THY1, HIF1A, LMOD1, ITGA1, DES, CSPG4, KCNK3, EGLN3 
       CD4, TGFB1, RERGL, ADAMTS1, XBP1, TNFAIP3, GPI, DUSP1, LGALS3BP, LAMC3 
Negative:  SVEP1, APOD, MFAP5, PI16, SCN7A, PLA2G2A, RORA, FBN1, CXCL14, DCLK1 
       TC2N, SFRP2, CD34, SEMA3C, PCOLCE2, RAMP2, CD38, F3, FGFR4, ASCL1 
       CLCA1, TCL1A, TP73, IL13, SMIM24, CXCR3, RNASE3, CX3CR1, LILRA4, FOXI1 
PC_ 4 
Positive:  INMT, SCN7A, TRPC6, LAMC3, NID1, HIGD1B, SVEP1, FGFR4, TSPAN8, RGS5 
       KCNK3, SPRY1, STEAP4, ITGA1, FKBP5, APOD, RORA, CRISPLD2, EGFR, TC2N 
       PDE4D, SLIT3, IFI6, LGALS3BP, SLC1A3, FAM184A, CD4, STAT4, WNT2, PRDX6 
Negative:  PLN, DES, CNN1, CTHRC1, THBS2, POSTN, MYH11, COL1A1, RERGL, LGR6 
       WT1, IGFBP3, FSCN1, FABP3, CDKN2A, PDPN, TGFB1, MEDAG, VEGFA, SLC2A1 
       LMOD1, CD24, HIF1A, NTRK2, RBP4, CXCL13, P2RX1, LYVE1, COL8A1, DIRAS3 
PC_ 5 
Positive:  IL6, CRISPLD2, IRF1, DUSP1, MYC, ADAMTS1, NFKB1, PIM1, KLF4, HIF1A 
       FGFR4, SLC1A3, ERN1, SCN7A, INMT, KLF5, SLC2A1, TNFAIP3, SVEP1, DES 
       DNAJB9, EGLN3, IRF8, FAM184A, VEGFA, PRDX6, SOX9, DPP6, LTBP2, RERGL 
Negative:  THY1, RGS5, LAMC3, HIGD1B, KCNK3, MFAP5, PI16, PCOLCE2, COL1A1, NTN4 
       PDGFRB, FBN1, LGALS3BP, GNG11, RAMP2, CD34, COL5A2, CTHRC1, FKBP5, TRPC6 
       CD24, IFI6, DCLK1, CXCL14, POSTN, WNT5A, IGFBP3, FABP3, CSPG4, CD4 
ipfatlas.xen1panel.fib.cells <- RunUMAP(ipfatlas.xen1panel.fib.cells, dims=1:30)
23:49:56 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
23:49:56 Read 6430 rows and found 30 numeric columns
23:49:56 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
23:49:56 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:49:56 Writing NN index file to temp file /tmp/RtmpvuYL8i/filef3dd7728f3e9
23:49:56 Searching Annoy index using 1 thread, search_k = 3000
23:49:58 Annoy recall = 100%
23:49:59 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
23:50:01 Initializing from normalized Laplacian + noise (using RSpectra)
23:50:01 Commencing optimization for 500 epochs, with 279884 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
23:50:09 Optimization finished
DimPlot(ipfatlas.xen1panel.fib.cells, group.by='Manuscript_Identity')

DimPlot(ipfatlas.xen1panel.fib.cells, group.by='Subclass_Cell_Identity')

DimPlot(ipfatlas.xen1panel.fib.cells, group.by='Subject_Identity')

DimPlot(ipfatlas.xen1panel.fib.cells, group.by='Disease_Identity')

Check fibroblast subtype markers

FeaturePlot(ipfatlas.xen1panel.fib.cells, features=c('COL1A1','CTHRC1','INMT','SCN7A'), order=T, max.cutoff = 'q99', min.cutoff='q1')

FeaturePlot(ipfatlas.xen1panel.fib.cells, features=c('PLA2G2A','PI16','MFAP5','WNT5A'), order=T, max.cutoff = 'q99', min.cutoff='q1')

Simulate Cosmx 1k panel

The “Universal” gene panel

cosmx.1Kpanel.genes <- read.csv('CosMx Human Universal Panel Gene Target List_sept23.csv', header = TRUE)$Gene
ipfatlas.cosmx1k.cells <- subset(ipfatlas.cells, features=cosmx.1Kpanel.genes)

Some genes in the Cosmx panel are not in the IPF dataset. Many of these genes are listed with a “/” suggesting that the probe cannot distinguish these isoforms. For simplicity I am not going to clean up the gene list. So actual list is a bit shorter. These are the missing genes:

setdiff(cosmx.1Kpanel.genes, rownames(ipfatlas.cells))
 [1] "BBLN"       "C11orf96"   "CALM2"      "CCL15"      "CCL3/L1/L3" "CCL4/L1/L2" "CD68"       "CLEC4A"    
 [9] "CXCL1/2/3"  "EIF5A/L1"   "FCGR3A/B"   "FKBP11"     "FLT3LG"     "H2AZ1"      "H4C3"       "HBA1/2"    
[17] "HCAR2/3"    "HILPDA"     "HLA-DQB1/2" "HLA-DRB"    "HSPA1A/B"   "IFITM1"     "IFNA1/13"   "IFNL2/3"   
[25] "IGKC"       "INS"        "KLRK1"      "KRT6A/B/C"  "LY75"       "MAP1LC3B/2" "MHC I"      "MIF"       
[33] "MZT2A/B"    "Negative01" "Negative02" "Negative03" "Negative04" "Negative05" "Negative06" "Negative07"
[41] "Negative08" "Negative09" "Negative10" "P2RX5"      "PDGFRA"     "PF4/V1"     "PTPRCAP"    "SAA1/2"    
[49] "SERPINA3"   "SOX2"       "TAP2"       "TNXA/B"     "TPSAB1/B2"  "XCL1/2"    

Standard clustering

ipfatlas.cosmx1k.cells <- NormalizeData(ipfatlas.cosmx1k.cells)
Normalizing layer: counts
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
ipfatlas.cosmx1k.cells <- FindVariableFeatures(ipfatlas.cosmx1k.cells)
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
ipfatlas.cosmx1k.cells <- ScaleData(ipfatlas.cosmx1k.cells, features=rownames(ipfatlas.cosmx1k.cells))
Centering and scaling data matrix

  |                                                                                                                           
  |                                                                                                                     |   0%
  |                                                                                                                           
  |=====================================================================================================================| 100%
ipfatlas.cosmx1k.cells <- RunPCA(ipfatlas.cosmx1k.cells)
PC_ 1 
Positive:  TYROBP, FCER1G, PSAP, HLA-DRA, GPX1, MARCO, GLUL, ACP5, CSTB, APOC1 
       C1QA, C1QB, OLR1, VIM, MRC1, C1QC, AIF1, CD74, SERPINA1, LGALS3 
       HLA-DPA1, GPNMB, MSR1, CXCL16, HLA-DPB1, LGALS1, APOE, S100A6, MS4A4A, RBM47 
Negative:  MALAT1, FYN, CD69, ITK, CCL5, ETS1, IL32, STAT4, RUNX3, GZMB 
       PRF1, CLEC2D, NKG7, GZMA, GNLY, CST7, CD3D, KLRB1, SPOCK2, CD2 
       CD3E, RORA, IKZF3, YES1, GZMH, TOX, CTSW, CD3G, ITM2A, KLRF1 
PC_ 2 
Positive:  SRGN, PTPRC, CXCR4, CD69, CCL5, STAT4, CD53, RUNX3, ITK, NKG7 
       ADGRE5, FYN, ARHGDIB, CST7, GZMB, CD3D, PRF1, DUSP2, GZMA, B2M 
       CD52, CD2, HCST, RGS1, GNLY, FYB1, TNFRSF1B, CD3E, KLRB1, GZMH 
Negative:  MGP, IGFBP7, SPARCL1, CALD1, CCDC80, COL1A2, RARRES2, EGFR, CAV1, CLU 
       LUM, PTK2, OSMR, COL5A2, COL1A1, COL6A1, DCN, COL3A1, IGFBP5, MYL9 
       COL6A2, BGN, CD59, SLPI, TM4SF1, COL6A3, COL4A1, KRT19, PLAC9, KRT18 
PC_ 3 
Positive:  CDH1, KRT19, CLDN4, TACSTD2, AGR2, EPCAM, KRT8, CXCL17, PIGR, KRT18 
       SLPI, CD24, CELSR1, KRT7, ADGRV1, IL20RA, EPHA2, LCN2, DDR1, ERBB3 
       FGFR2, ITGA3, ALCAM, TNFRSF19, WIF1, GDF15, EFNA5, IL7, MET, ITGB6 
Negative:  COL1A2, LUM, COL3A1, COL6A2, DCN, COL6A3, BGN, COL6A1, MGP, COL1A1 
       SPARCL1, CALD1, COL5A2, RARRES2, COL5A1, MXRA8, IGFBP6, CDH11, PTGDS, MEG3 
       THBS2, TPM2, CACNA1C, DPT, PDGFRB, CXCL12, PTGIS, FGF7, MMP2, COL4A1 
PC_ 4 
Positive:  B2M, CCL5, CD52, CTSW, IL32, CD3D, MYL12A, GZMB, GZMA, RPL32 
       FAU, GZMH, CD69, CRIP1, ARHGDIB, FABP4, CD3E, PRF1, RPL37, ITK 
       NKG7, SOD1, CD2, RPL34, CALM1, GNLY, RPL21, CST7, IL6ST, KLRB1 
Negative:  NLRP3, IL1R2, VCAN, HIF1A, NEAT1, THBS1, SAT1, ITGAX, CSF2RA, VEGFA 
       BASP1, TIMP1, ADGRE2, GPR183, SOD2, G0S2, MAP2K1, CD93, TLR2, CSF3R 
       MALAT1, RBM47, AREG, ST6GALNAC3, RGS2, CXCL8, CD14, IL1R1, S100A8, PPARD 
PC_ 5 
Positive:  RPL21, RPL34, RPL32, RPL37, IL6ST, FAU, UBA52, RACK1, RPL22, GPR183 
       NACA, BTF3, AREG, ZFP36, ATP5F1E, RGS2, JUNB, TPT1, SRGN, TIMP1 
       RGS1, NLRP3, PFN1, CXCR4, B2M, SEC61G, IL1R2, HMGB2, VSIR, TPI1 
Negative:  FABP4, MSR1, INHBA, PPARG, C1QB, C1QA, APOC1, DST, C1QC, LGALS3BP 
       APOE, NUPR1, MARCO, OASL, FN1, IFI6, CD5L, ITGB8, CCL18, ACP5 
       IFI27, SLCO2B1, CXCL5, ACACB, FFAR4, MRC1, PDGFC, DDX58, GPNMB, DCN 
ipfatlas.cosmx1k.cells <- RunUMAP(ipfatlas.cosmx1k.cells, dims=1:30)
00:18:10 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:18:10 Read 253424 rows and found 30 numeric columns
00:18:10 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:18:10 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:18:51 Writing NN index file to temp file /tmp/RtmpvuYL8i/filef3dd3e1559c9
00:18:51 Searching Annoy index using 1 thread, search_k = 3000
00:20:42 Annoy recall = 100%
00:20:43 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
00:20:57 Initializing from normalized Laplacian + noise (using RSpectra)
00:22:01 Commencing optimization for 200 epochs, with 11742532 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:24:24 Optimization finished
DimPlot(ipfatlas.cosmx1k.cells, group.by='CellType_Category')
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

DimPlot(ipfatlas.cosmx1k.cells, group.by='Manuscript_Identity')
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

DimPlot(ipfatlas.cosmx1k.cells, group.by='Subclass_Cell_Identity')
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

DimPlot(ipfatlas.cosmx1k.cells, group.by='Subject_Identity')
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

DimPlot(ipfatlas.cosmx1k.cells, group.by='Disease_Identity')
Rasterizing points since number of points exceeds 100,000.
To disable this behavior set `raster=FALSE`

Subset epithelium

ipfatlas.cosmx1k.epi.cells <- subset(ipfatlas.cosmx1k.cells, subset=CellType_Category=='Epithelial')
ipfatlas.cosmx1k.epi.cells <- NormalizeData(ipfatlas.cosmx1k.epi.cells)
Normalizing layer: counts
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
ipfatlas.cosmx1k.epi.cells <- FindVariableFeatures(ipfatlas.cosmx1k.epi.cells)
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
ipfatlas.cosmx1k.epi.cells <- ScaleData(ipfatlas.cosmx1k.epi.cells, features=rownames(ipfatlas.cosmx1k.epi.cells))
Centering and scaling data matrix

  |                                                                                                                           
  |                                                                                                                     |   0%
  |                                                                                                                           
  |=====================================================================================================================| 100%
ipfatlas.cosmx1k.epi.cells <- RunPCA(ipfatlas.cosmx1k.epi.cells)
Warning: The following 3 features requested have zero variance; running reduction without them: PDS5A, CTSD, CRPPC_ 1 
Positive:  MALAT1, CD44, CSF3R, BMP1, ROR1, SORBS1, LDLR, NFKBIA, ITGB6, IL1R1 
       LAMP3, COL8A1, FGFR1, TTN, DPP4, ETV5, OSMR, TNFRSF10B, EFNB2, BMP2 
       NDRG1, HIF1A, CD83, COL12A1, NFKB1, ITGA9, CXCL8, ADGRF5, VEGFA, SOD2 
Negative:  GSTP1, CALM1, CRIP1, B2M, CD24, SOD1, CD59, HSP90AA1, ATP5F1E, TUBB4B 
       IGFBP7, CSTB, PLAC8, RPL34, S100A6, FAU, IL6ST, LGALS3, HSP90AB1, RPL21 
       RPL32, UBA52, CLU, GSN, BASP1, IGFBP5, GLUL, TPT1, KRT8, PTGES3 
PC_ 2 
Positive:  IL7, PLAC8, COL21A1, IGF1R, BASP1, PSD3, IL16, NELL2, IGFBP7, TNFRSF19 
       MAML2, CELSR1, RARB, CD24, IGFBP5, ALCAM, BRCA1, ATR, NRXN3, DLL1 
       CRIP1, WNT5B, ITGB8, PSCA, NPR2, HSP90AA1, CASP8, RGS5, ATM, TSHZ2 
Negative:  NFKBIA, TM4SF1, FABP5, IFITM3, RPL37, RACK1, TPT1, CXCL8, RPL32, CD44 
       LAMP3, ENO1, RPL22, CXCL17, RPL21, PHLDA2, NACA, SERPINA1, CD63, FAU 
       RPL34, DUSP6, UBA52, LDHA, ZFP36, MT1X, BTF3, AREG, MYC, BMP2 
PC_ 3 
Positive:  LAMP3, CSF3R, SLPI, WIF1, TOX, FABP5, PIGR, CD36, SERPINA1, CXCL17 
       CD74, TTN, HLA-DPA1, FASN, HLA-DRA, CCL20, HLA-DPB1, BMP1, DMBT1, ETV5 
       CD83, CD38, CSF3, RBM47, SOD2, SPRY4, ADGRV1, AGR2, IL20RA, BMP2 
Negative:  DST, KRT17, CAV1, S100A2, SPOCK2, KRT15, MIR4435-2HG, KRT5, CALD1, KRT7 
       MYL9, IL32, COL4A2, CEACAM6, COL6A2, KRT19, CYTOR, COL4A1, PXDN, TIMP1 
       ANKRD1, ROR1, IGFBP6, NRG1, GAS6, TNNC1, AXL, COL17A1, TNFRSF12A, S100A10 
PC_ 4 
Positive:  SPOCK2, TNNC1, MYL9, CAV1, ANKRD1, COL4A2, VEGFA, COL8A1, COL4A1, CEACAM6 
       IL32, S100A6, ROR1, B2M, IFI27, COL12A1, GAS6, S100A4, GSTP1, ATP5F1E 
       NUPR1, RGCC, MYL12A, S100A10, KRT7, RPL21, SLC40A1, CRIP1, CD63, IFI6 
Negative:  EFNA5, MET, KRT17, NEAT1, PSD3, SQSTM1, PTK2, GLUD1, S100A2, ITGB8 
       TNFRSF10B, KRT5, RORA, TNFRSF21, EPHA2, LYN, KRT15, MAML2, SMAD3, ARID5B 
       IL1R1, RBM47, SERPINB5, CDH1, STAT3, NFKB1, CTNNB1, SAT1, IL4R, SLC2A1 
PC_ 5 
Positive:  CDH1, NEAT1, ALCAM, CD55, CFLAR, SPOCK2, TNNC1, COL4A2, COL12A1, ANKRD1 
       BCL2L1, ITGA3, LDLR, ITGB6, MECOM, MALAT1, IL32, CLDN4, LYN, COL4A1 
       STAT3, VEGFA, ATF3, BRAF, ADGRF5, CAV1, MYL9, KRT7, EFNA1, GADD45B 
Negative:  TIMP1, PTGIS, VCAM1, COL6A2, SLPI, RPL34, MGP, RPL32, COL1A2, RARRES1 
       CCL2, CLU, RPL21, SCGB3A1, TPM2, MT1X, NACA, IGFBP6, PTGDS, LTF 
       BTF3, THBS2, TPT1, LAMA4, FAU, IL6ST, COL3A1, MT2A, RPS4Y1, IL6 
ipfatlas.cosmx1k.epi.cells <- RunUMAP(ipfatlas.cosmx1k.epi.cells, dims=1:30)
00:24:57 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:24:57 Read 13370 rows and found 30 numeric columns
00:24:57 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:24:57 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:24:59 Writing NN index file to temp file /tmp/RtmpvuYL8i/filef3dd24bad2d6
00:24:59 Searching Annoy index using 1 thread, search_k = 3000
00:25:02 Annoy recall = 100%
00:25:04 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
00:25:06 Initializing from normalized Laplacian + noise (using RSpectra)
00:25:06 Commencing optimization for 200 epochs, with 607432 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:25:13 Optimization finished
DimPlot(ipfatlas.cosmx1k.epi.cells, group.by='Manuscript_Identity')

DimPlot(ipfatlas.cosmx1k.epi.cells, group.by='Subclass_Cell_Identity')

DimPlot(ipfatlas.cosmx1k.epi.cells, group.by='Subject_Identity')

DimPlot(ipfatlas.cosmx1k.epi.cells, group.by='Disease_Identity')

Check fibrotic DATP expression

Subset fibroblasts

ipfatlas.cosmx1k.fib.cells <- subset(ipfatlas.cosmx1k.cells, subset=CellType_Category=='Stromal')
ipfatlas.cosmx1k.fib.cells <- NormalizeData(ipfatlas.cosmx1k.fib.cells)
Normalizing layer: counts
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
ipfatlas.cosmx1k.fib.cells <- FindVariableFeatures(ipfatlas.cosmx1k.fib.cells)
Finding variable features for layer counts
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
ipfatlas.cosmx1k.fib.cells <- ScaleData(ipfatlas.cosmx1k.fib.cells, features=rownames(ipfatlas.cosmx1k.fib.cells))
Centering and scaling data matrix

  |                                                                                                                           
  |                                                                                                                     |   0%
  |                                                                                                                           
  |=====================================================================================================================| 100%
ipfatlas.cosmx1k.fib.cells <- RunPCA(ipfatlas.cosmx1k.fib.cells)
Warning: The following 6 features requested have zero variance; running reduction without them: IL22RA1, LAIR1, ATG10, RBPJ, RAC1, MYL7PC_ 1 
Positive:  S100A10, DCN, CCDC80, MMP2, CFD, LUM, IGFBP6, ANXA2, S100A6, COL1A2 
       TIMP1, ANXA1, LGALS3, MFAP5, PTGIS, EMP3, RARRES1, VCAN, GPNMB, LDHA 
       CD63, VIM, COL6A1, GSN, PSAP, TSHZ2, CYP1B1, COL1A1, TPT1, RACK1 
Negative:  MALAT1, MYH11, COX4I2, NOTCH3, RGS5, ADGRF5, NDUFA4L2, ACTG2, COL4A1, CARMN 
       ESAM, CACNA1C, ITGA1, PTEN, RYR2, CSPG4, COL4A2, FKBP5, ACTA2, TNNT2 
       TPM2, CALD1, PDGFRB, ANGPT2, ZBTB16, PDGFA, ARHGDIB, TM4SF1, LYVE1, MYL9 
PC_ 2 
Positive:  CFD, MFAP5, CCDC80, SLPI, DCN, APOD, PTGIS, IGFBP6, CLU, CD34 
       IGF2, ACKR3, TSHZ2, ITM2A, CD55, SOD2, FGFR1, NPR1, CXCL14, ABL2 
       MEG3, GSN, VEGFA, ESR1, TGFBR2, DDR2, GPNMB, RSPO3, EGFR, TWIST2 
Negative:  MYL9, TPM2, BGN, NDUFA4L2, COX4I2, ACTA2, NOTCH3, CALD1, CAV1, RGS5 
       LGALS1, IGFBP7, TSC22D1, PFN1, ARHGDIB, ACTG2, ITGA1, CALM1, COL4A1, MYH11 
       ESAM, ADGRF5, TPM1, CACNA1C, PDGFRB, HSPB1, COL4A2, TPI1, NACA, VIM 
PC_ 3 
Positive:  COL6A3, CDH11, COL3A1, COL1A1, COL5A2, COL8A1, ITGA8, FN1, BMP5, COL1A2 
       RYR2, COL5A1, LUM, ROR1, RORA, FAP, MMP2, PTGDS, PSD3, DST 
       TNFRSF19, TSHZ2, COL16A1, EPHA3, ENTPD1, VCAN, IL16, LDB2, ITGB8, ANGPT1 
Negative:  GPX3, IGFBP6, SLPI, MFAP5, MT2A, GSN, IGFBP5, MYC, ZFP36, LDHA 
       ACKR3, CDKN1A, RPL21, CRIP1, MYH11, JUNB, CLU, CRYAB, CD34, NDUFA4L2 
       IGFBP7, COX4I2, MT1X, ITM2A, FAU, IGF2, RAMP2, GADD45B, ACTG2, IFI27 
PC_ 4 
Positive:  MGP, NUPR1, DCN, ITM2B, SPARCL1, CFD, S100A6, LUM, RPL34, RPL21 
       CLU, PLAC9, CD63, RARRES2, B2M, RPL32, IL16, GSN, LGALS1, S100A4 
       ITM2A, APOD, IFITM3, CXCL14, GPX3, GSTP1, UBA52, FAU, PTGDS, SLC40A1 
Negative:  MIR4435-2HG, CYTOR, THBS1, CD44, ABL2, NFKB1, HIF1A, VEGFA, TNFRSF10B, CDKN1A 
       IL1R1, SOD2, ITGAV, NEAT1, ETS1, LDLR, TNFRSF12A, MAP2K1, FGFR1, ATF3 
       GADD45B, MYC, DUSP5, IL6, LIF, CCL2, BAG3, PPARD, FGF2, STAT3 
PC_ 5 
Positive:  PTGDS, CCL2, RARRES2, ZFP36, SAT1, JUNB, SOD2, MT2A, MT1X, GADD45B 
       IL6ST, MGP, RGCC, RPL34, FOS, RPL21, CXCL12, NFKBIA, IL6, RPL32 
       TNFSF13B, DUSP1, ANGPT1, SELENOP, THBS1, LUM, SRGN, ITGA2, ITGA8, JUN 
Negative:  MFAP5, COL12A1, COL1A2, IGFBP5, COL3A1, PTGIS, IGFBP6, ACKR3, SLPI, CRIP1 
       COL1A1, CD34, THBS2, COL5A1, TGFBI, CD55, IFI27, APP, MEG3, PTK2 
       IGF2, CALD1, CD276, BMP1, COL6A1, MMP14, SH3BGRL3, PDGFRB, COL6A2, GSN 
ipfatlas.cosmx1k.fib.cells <- RunUMAP(ipfatlas.cosmx1k.fib.cells, dims=1:30)
00:25:30 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:25:30 Read 6430 rows and found 30 numeric columns
00:25:30 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
00:25:30 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:25:31 Writing NN index file to temp file /tmp/RtmpvuYL8i/filef3dd5dd09376
00:25:31 Searching Annoy index using 1 thread, search_k = 3000
00:25:33 Annoy recall = 100%
00:25:34 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
00:25:36 Initializing from normalized Laplacian + noise (using RSpectra)
00:25:36 Commencing optimization for 500 epochs, with 275020 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
00:25:44 Optimization finished
DimPlot(ipfatlas.cosmx1k.fib.cells, group.by='Manuscript_Identity')

DimPlot(ipfatlas.cosmx1k.fib.cells, group.by='Subclass_Cell_Identity')

DimPlot(ipfatlas.cosmx1k.fib.cells, group.by='Subject_Identity')

DimPlot(ipfatlas.cosmx1k.fib.cells, group.by='Disease_Identity')

Check fibroblast subtype markers

FeaturePlot(ipfatlas.cosmx1k.fib.cells, features=c('COL1A1','CTHRC1','INMT','SCN7A'), order=T, max.cutoff = 'q99', min.cutoff='q1')
Warning: The following requested variables were not found: CTHRC1, INMT, SCN7A

FeaturePlot(ipfatlas.cosmx1k.fib.cells, features=c('PLA2G2A','PI16','MFAP5','WNT5A'), order=T, max.cutoff = 'q99', min.cutoff='q1')
Warning: The following requested variables were not found: PLA2G2A, PI16

---
title: "Simulation of restricted probesets for spatial transcriptomics"
output:
  github_document:
    toc: true
  html_notebook:
    toc: true
---

For probe-based spatial transcriptomics, the number and identity of genes that have probesets is limited. This script queries how this restricted gene set might perform in cell identification by routine clustering.

# Environment
```{r message=FALSE, warning=FALSE}
library(tidyverse)
library(Seurat)
library(SeuratWrappers)
library(monocle3)
library(RColorBrewer)
spectral.colors <- colorRampPalette(rev(brewer.pal(9,'Spectral')))
```

## Read in IPF Cell Atlas (GSE136831)
This notebook depends on the output of the read-in script. for convenience, can bypass script by loading stored Seurat object directly.
```{r}
# This is more reliable
#source(knitr::purl("IPF-Cell-Atlas__read-in.Rmd", quiet=TRUE))
knitr::knit("IPF-Cell-Atlas__read-in.Rmd", output = tempfile())
```
```{r, eval=FALSE}
# faster
load('ipfatlas.cells_fromd8d92b9.Robj')
```

# Simulate Xenium lung panel 1
This is the panel used in the initial trial runs with the genomics CoLabs.
```{r}
xenium.panel1.genes <- read.csv('Xenium_panel_order_08_10_23.csv', header = TRUE)$Gene
```
```{r}
ipfatlas.xen1panel.cells <- subset(ipfatlas.cells, features=xenium.panel1.genes)
```
Some genes in the xenium panel are not in the IPF dataset. Not sure why the dataset in GSE136831 is missing these genes. so actual list is a bit shorter. These are the missing genes:
```{r}
setdiff(xenium.panel1.genes, rownames(ipfatlas.xen1panel.cells))
```

## Standard clustering
```{r}
ipfatlas.xen1panel.cells <- NormalizeData(ipfatlas.xen1panel.cells)
ipfatlas.xen1panel.cells <- FindVariableFeatures(ipfatlas.xen1panel.cells)
ipfatlas.xen1panel.cells <- ScaleData(ipfatlas.xen1panel.cells, features=rownames(ipfatlas.xen1panel.cells))
ipfatlas.xen1panel.cells <- RunPCA(ipfatlas.xen1panel.cells)
ipfatlas.xen1panel.cells <- RunUMAP(ipfatlas.xen1panel.cells, dims=1:30)
```
```{r}
DimPlot(ipfatlas.xen1panel.cells, group.by='CellType_Category')
DimPlot(ipfatlas.xen1panel.cells, group.by='Manuscript_Identity')
DimPlot(ipfatlas.xen1panel.cells, group.by='Subclass_Cell_Identity')
DimPlot(ipfatlas.xen1panel.cells, group.by='Subject_Identity')
DimPlot(ipfatlas.xen1panel.cells, group.by='Disease_Identity')
```

## Subset epithelium
```{r}
ipfatlas.xen1panel.epi.cells <- subset(ipfatlas.xen1panel.cells, subset=CellType_Category=='Epithelial')
```

```{r}
ipfatlas.xen1panel.epi.cells <- NormalizeData(ipfatlas.xen1panel.epi.cells)
ipfatlas.xen1panel.epi.cells <- FindVariableFeatures(ipfatlas.xen1panel.epi.cells)
ipfatlas.xen1panel.epi.cells <- ScaleData(ipfatlas.xen1panel.epi.cells, features=rownames(ipfatlas.xen1panel.epi.cells))
ipfatlas.xen1panel.epi.cells <- RunPCA(ipfatlas.xen1panel.epi.cells)
ipfatlas.xen1panel.epi.cells <- RunUMAP(ipfatlas.xen1panel.epi.cells, dims=1:30)
```
```{r}
DimPlot(ipfatlas.xen1panel.epi.cells, group.by='Manuscript_Identity')
DimPlot(ipfatlas.xen1panel.epi.cells, group.by='Subclass_Cell_Identity')
DimPlot(ipfatlas.xen1panel.epi.cells, group.by='Subject_Identity')
DimPlot(ipfatlas.xen1panel.epi.cells, group.by='Disease_Identity')
```
Check fibrotic DATP expression
```{r}
FeaturePlot(ipfatlas.xen1panel.epi.cells, features=c('CLDN4','KRT7','LGALS3','GDF15'), order=T, max.cutoff = 'q99', min.cutoff='q1')
FeaturePlot(ipfatlas.xen1panel.epi.cells, features=c('KRT17','KRT5','TP63','PDPN'), order=T, max.cutoff = 'q99', min.cutoff='q1')
```

## Subset fibroblasts
```{r}
ipfatlas.xen1panel.fib.cells <- subset(ipfatlas.xen1panel.cells, subset=CellType_Category=='Stromal')
```

```{r}
ipfatlas.xen1panel.fib.cells <- NormalizeData(ipfatlas.xen1panel.fib.cells)
ipfatlas.xen1panel.fib.cells <- FindVariableFeatures(ipfatlas.xen1panel.fib.cells)
ipfatlas.xen1panel.fib.cells <- ScaleData(ipfatlas.xen1panel.fib.cells, features=rownames(ipfatlas.xen1panel.fib.cells))
ipfatlas.xen1panel.fib.cells <- RunPCA(ipfatlas.xen1panel.fib.cells)
ipfatlas.xen1panel.fib.cells <- RunUMAP(ipfatlas.xen1panel.fib.cells, dims=1:30)
```
```{r}
DimPlot(ipfatlas.xen1panel.fib.cells, group.by='Manuscript_Identity')
DimPlot(ipfatlas.xen1panel.fib.cells, group.by='Subclass_Cell_Identity')
DimPlot(ipfatlas.xen1panel.fib.cells, group.by='Subject_Identity')
DimPlot(ipfatlas.xen1panel.fib.cells, group.by='Disease_Identity')
```
Check fibroblast subtype markers
```{r}
FeaturePlot(ipfatlas.xen1panel.fib.cells, features=c('COL1A1','CTHRC1','INMT','SCN7A'), order=T, max.cutoff = 'q99', min.cutoff='q1')
FeaturePlot(ipfatlas.xen1panel.fib.cells, features=c('PLA2G2A','PI16','MFAP5','WNT5A'), order=T, max.cutoff = 'q99', min.cutoff='q1')
```

# Simulate Cosmx 1k panel
The "Universal" gene panel
```{r}
cosmx.1Kpanel.genes <- read.csv('CosMx Human Universal Panel Gene Target List_sept23.csv', header = TRUE)$Gene
```
```{r}
ipfatlas.cosmx1k.cells <- subset(ipfatlas.cells, features=cosmx.1Kpanel.genes)
```
Some genes in the Cosmx panel are not in the IPF dataset. Many of these genes are listed with a "/" suggesting that the probe cannot distinguish these isoforms. For simplicity I am not going to clean up the gene list. So actual list is a bit shorter. These are the missing genes:
```{r}
setdiff(cosmx.1Kpanel.genes, rownames(ipfatlas.cells))
```

## Standard clustering
```{r}
ipfatlas.cosmx1k.cells <- NormalizeData(ipfatlas.cosmx1k.cells)
ipfatlas.cosmx1k.cells <- FindVariableFeatures(ipfatlas.cosmx1k.cells)
ipfatlas.cosmx1k.cells <- ScaleData(ipfatlas.cosmx1k.cells, features=rownames(ipfatlas.cosmx1k.cells))
ipfatlas.cosmx1k.cells <- RunPCA(ipfatlas.cosmx1k.cells)
ipfatlas.cosmx1k.cells <- RunUMAP(ipfatlas.cosmx1k.cells, dims=1:30)
```
```{r}
DimPlot(ipfatlas.cosmx1k.cells, group.by='CellType_Category')
DimPlot(ipfatlas.cosmx1k.cells, group.by='Manuscript_Identity')
DimPlot(ipfatlas.cosmx1k.cells, group.by='Subclass_Cell_Identity')
DimPlot(ipfatlas.cosmx1k.cells, group.by='Subject_Identity')
DimPlot(ipfatlas.cosmx1k.cells, group.by='Disease_Identity')
```

## Subset epithelium
```{r}
ipfatlas.cosmx1k.epi.cells <- subset(ipfatlas.cosmx1k.cells, subset=CellType_Category=='Epithelial')
```

```{r}
ipfatlas.cosmx1k.epi.cells <- NormalizeData(ipfatlas.cosmx1k.epi.cells)
ipfatlas.cosmx1k.epi.cells <- FindVariableFeatures(ipfatlas.cosmx1k.epi.cells)
ipfatlas.cosmx1k.epi.cells <- ScaleData(ipfatlas.cosmx1k.epi.cells, features=rownames(ipfatlas.cosmx1k.epi.cells))
ipfatlas.cosmx1k.epi.cells <- RunPCA(ipfatlas.cosmx1k.epi.cells)
ipfatlas.cosmx1k.epi.cells <- RunUMAP(ipfatlas.cosmx1k.epi.cells, dims=1:30)
```
```{r}
DimPlot(ipfatlas.cosmx1k.epi.cells, group.by='Manuscript_Identity')
DimPlot(ipfatlas.cosmx1k.epi.cells, group.by='Subclass_Cell_Identity')
DimPlot(ipfatlas.cosmx1k.epi.cells, group.by='Subject_Identity')
DimPlot(ipfatlas.cosmx1k.epi.cells, group.by='Disease_Identity')
```
Check fibrotic DATP expression
```{r}
FeaturePlot(ipfatlas.cosmx1k.epi.cells, features=c('CLDN4','KRT7','LGALS3','GDF15'), order=T, max.cutoff = 'q99', min.cutoff='q1')
FeaturePlot(ipfatlas.cosmx1k.epi.cells, features=c('KRT17','KRT5','TP63','PDPN'), order=T, max.cutoff = 'q99', min.cutoff='q1')
```

## Subset fibroblasts
```{r}
ipfatlas.cosmx1k.fib.cells <- subset(ipfatlas.cosmx1k.cells, subset=CellType_Category=='Stromal')
```

```{r}
ipfatlas.cosmx1k.fib.cells <- NormalizeData(ipfatlas.cosmx1k.fib.cells)
ipfatlas.cosmx1k.fib.cells <- FindVariableFeatures(ipfatlas.cosmx1k.fib.cells)
ipfatlas.cosmx1k.fib.cells <- ScaleData(ipfatlas.cosmx1k.fib.cells, features=rownames(ipfatlas.cosmx1k.fib.cells))
ipfatlas.cosmx1k.fib.cells <- RunPCA(ipfatlas.cosmx1k.fib.cells)
ipfatlas.cosmx1k.fib.cells <- RunUMAP(ipfatlas.cosmx1k.fib.cells, dims=1:30)
```
```{r}
DimPlot(ipfatlas.cosmx1k.fib.cells, group.by='Manuscript_Identity')
DimPlot(ipfatlas.cosmx1k.fib.cells, group.by='Subclass_Cell_Identity')
DimPlot(ipfatlas.cosmx1k.fib.cells, group.by='Subject_Identity')
DimPlot(ipfatlas.cosmx1k.fib.cells, group.by='Disease_Identity')
```
Check fibroblast subtype markers
```{r}
FeaturePlot(ipfatlas.cosmx1k.fib.cells, features=c('COL1A1','CTHRC1','INMT','SCN7A'), order=T, max.cutoff = 'q99', min.cutoff='q1')
FeaturePlot(ipfatlas.cosmx1k.fib.cells, features=c('PLA2G2A','PI16','MFAP5','WNT5A'), order=T, max.cutoff = 'q99', min.cutoff='q1')
```